# coding=utf-8
# Copyright 2022 Apple Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Original license: https://github.com/apple/ml-cvnets/blob/main/LICENSE
"""PyTorch MobileViT model."""

import math
from typing import Optional, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ...activations import ACT2FN
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
    SemanticSegmenterOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import auto_docstring, logging, torch_int
from .configuration_mobilevit import MobileViTConfig


logger = logging.get_logger(__name__)


def make_divisible(value: int, divisor: int = 8, min_value: Optional[int] = None) -> int:
    """
    Ensure that all layers have a channel count that is divisible by `divisor`.
    """
    if min_value is None:
        min_value = divisor
    new_value = max(min_value, int(value + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_value < 0.9 * value:
        new_value += divisor
    return int(new_value)


class MobileViTConvLayer(nn.Module):
    def __init__(
        self,
        config: MobileViTConfig,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        stride: int = 1,
        groups: int = 1,
        bias: bool = False,
        dilation: int = 1,
        use_normalization: bool = True,
        use_activation: Union[bool, str] = True,
    ) -> None:
        super().__init__()
        padding = int((kernel_size - 1) / 2) * dilation

        if in_channels % groups != 0:
            raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups.")
        if out_channels % groups != 0:
            raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups.")

        self.convolution = nn.Conv2d(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=groups,
            bias=bias,
            padding_mode="zeros",
        )

        if use_normalization:
            self.normalization = nn.BatchNorm2d(
                num_features=out_channels,
                eps=1e-5,
                momentum=0.1,
                affine=True,
                track_running_stats=True,
            )
        else:
            self.normalization = None

        if use_activation:
            if isinstance(use_activation, str):
                self.activation = ACT2FN[use_activation]
            elif isinstance(config.hidden_act, str):
                self.activation = ACT2FN[config.hidden_act]
            else:
                self.activation = config.hidden_act
        else:
            self.activation = None

    def forward(self, features: torch.Tensor) -> torch.Tensor:
        features = self.convolution(features)
        if self.normalization is not None:
            features = self.normalization(features)
        if self.activation is not None:
            features = self.activation(features)
        return features


class MobileViTInvertedResidual(nn.Module):
    """
    Inverted residual block (MobileNetv2): https://huggingface.co/papers/1801.04381
    """

    def __init__(
        self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int, dilation: int = 1
    ) -> None:
        super().__init__()
        expanded_channels = make_divisible(int(round(in_channels * config.expand_ratio)), 8)

        if stride not in [1, 2]:
            raise ValueError(f"Invalid stride {stride}.")

        self.use_residual = (stride == 1) and (in_channels == out_channels)

        self.expand_1x1 = MobileViTConvLayer(
            config, in_channels=in_channels, out_channels=expanded_channels, kernel_size=1
        )

        self.conv_3x3 = MobileViTConvLayer(
            config,
            in_channels=expanded_channels,
            out_channels=expanded_channels,
            kernel_size=3,
            stride=stride,
            groups=expanded_channels,
            dilation=dilation,
        )

        self.reduce_1x1 = MobileViTConvLayer(
            config,
            in_channels=expanded_channels,
            out_channels=out_channels,
            kernel_size=1,
            use_activation=False,
        )

    def forward(self, features: torch.Tensor) -> torch.Tensor:
        residual = features

        features = self.expand_1x1(features)
        features = self.conv_3x3(features)
        features = self.reduce_1x1(features)

        return residual + features if self.use_residual else features


class MobileViTMobileNetLayer(nn.Module):
    def __init__(
        self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int = 1, num_stages: int = 1
    ) -> None:
        super().__init__()

        self.layer = nn.ModuleList()
        for i in range(num_stages):
            layer = MobileViTInvertedResidual(
                config,
                in_channels=in_channels,
                out_channels=out_channels,
                stride=stride if i == 0 else 1,
            )
            self.layer.append(layer)
            in_channels = out_channels

    def forward(self, features: torch.Tensor) -> torch.Tensor:
        for layer_module in self.layer:
            features = layer_module(features)
        return features


class MobileViTSelfAttention(nn.Module):
    def __init__(self, config: MobileViTConfig, hidden_size: int) -> None:
        super().__init__()

        if hidden_size % config.num_attention_heads != 0:
            raise ValueError(
                f"The hidden size {hidden_size} is not a multiple of the number of attention "
                f"heads {config.num_attention_heads}."
            )

        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(hidden_size, self.all_head_size, bias=config.qkv_bias)
        self.key = nn.Linear(hidden_size, self.all_head_size, bias=config.qkv_bias)
        self.value = nn.Linear(hidden_size, self.all_head_size, bias=config.qkv_bias)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        batch_size, seq_length, _ = hidden_states.shape
        query_layer = (
            self.query(hidden_states)
            .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
            .transpose(1, 2)
        )
        key_layer = (
            self.key(hidden_states)
            .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
            .transpose(1, 2)
        )
        value_layer = (
            self.value(hidden_states)
            .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
            .transpose(1, 2)
        )

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)

        # Normalize the attention scores to probabilities.
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs)

        context_layer = torch.matmul(attention_probs, value_layer)

        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(*new_context_layer_shape)
        return context_layer


class MobileViTSelfOutput(nn.Module):
    def __init__(self, config: MobileViTConfig, hidden_size: int) -> None:
        super().__init__()
        self.dense = nn.Linear(hidden_size, hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states


class MobileViTAttention(nn.Module):
    def __init__(self, config: MobileViTConfig, hidden_size: int) -> None:
        super().__init__()
        self.attention = MobileViTSelfAttention(config, hidden_size)
        self.output = MobileViTSelfOutput(config, hidden_size)
        self.pruned_heads = set()

    def prune_heads(self, heads: set[int]) -> None:
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
        )

        # Prune linear layers
        self.attention.query = prune_linear_layer(self.attention.query, index)
        self.attention.key = prune_linear_layer(self.attention.key, index)
        self.attention.value = prune_linear_layer(self.attention.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

        # Update hyper params and store pruned heads
        self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
        self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        self_outputs = self.attention(hidden_states)
        attention_output = self.output(self_outputs)
        return attention_output


class MobileViTIntermediate(nn.Module):
    def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None:
        super().__init__()
        self.dense = nn.Linear(hidden_size, intermediate_size)
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states


class MobileViTOutput(nn.Module):
    def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None:
        super().__init__()
        self.dense = nn.Linear(intermediate_size, hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = hidden_states + input_tensor
        return hidden_states


class MobileViTTransformerLayer(nn.Module):
    def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None:
        super().__init__()
        self.attention = MobileViTAttention(config, hidden_size)
        self.intermediate = MobileViTIntermediate(config, hidden_size, intermediate_size)
        self.output = MobileViTOutput(config, hidden_size, intermediate_size)
        self.layernorm_before = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
        self.layernorm_after = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        attention_output = self.attention(self.layernorm_before(hidden_states))
        hidden_states = attention_output + hidden_states

        layer_output = self.layernorm_after(hidden_states)
        layer_output = self.intermediate(layer_output)
        layer_output = self.output(layer_output, hidden_states)
        return layer_output


class MobileViTTransformer(nn.Module):
    def __init__(self, config: MobileViTConfig, hidden_size: int, num_stages: int) -> None:
        super().__init__()

        self.layer = nn.ModuleList()
        for _ in range(num_stages):
            transformer_layer = MobileViTTransformerLayer(
                config,
                hidden_size=hidden_size,
                intermediate_size=int(hidden_size * config.mlp_ratio),
            )
            self.layer.append(transformer_layer)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        for layer_module in self.layer:
            hidden_states = layer_module(hidden_states)
        return hidden_states


class MobileViTLayer(GradientCheckpointingLayer):
    """
    MobileViT block: https://huggingface.co/papers/2110.02178
    """

    def __init__(
        self,
        config: MobileViTConfig,
        in_channels: int,
        out_channels: int,
        stride: int,
        hidden_size: int,
        num_stages: int,
        dilation: int = 1,
    ) -> None:
        super().__init__()
        self.patch_width = config.patch_size
        self.patch_height = config.patch_size

        if stride == 2:
            self.downsampling_layer = MobileViTInvertedResidual(
                config,
                in_channels=in_channels,
                out_channels=out_channels,
                stride=stride if dilation == 1 else 1,
                dilation=dilation // 2 if dilation > 1 else 1,
            )
            in_channels = out_channels
        else:
            self.downsampling_layer = None

        self.conv_kxk = MobileViTConvLayer(
            config,
            in_channels=in_channels,
            out_channels=in_channels,
            kernel_size=config.conv_kernel_size,
        )

        self.conv_1x1 = MobileViTConvLayer(
            config,
            in_channels=in_channels,
            out_channels=hidden_size,
            kernel_size=1,
            use_normalization=False,
            use_activation=False,
        )

        self.transformer = MobileViTTransformer(
            config,
            hidden_size=hidden_size,
            num_stages=num_stages,
        )

        self.layernorm = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)

        self.conv_projection = MobileViTConvLayer(
            config, in_channels=hidden_size, out_channels=in_channels, kernel_size=1
        )

        self.fusion = MobileViTConvLayer(
            config, in_channels=2 * in_channels, out_channels=in_channels, kernel_size=config.conv_kernel_size
        )

    def unfolding(self, features: torch.Tensor) -> tuple[torch.Tensor, dict]:
        patch_width, patch_height = self.patch_width, self.patch_height
        patch_area = int(patch_width * patch_height)

        batch_size, channels, orig_height, orig_width = features.shape

        new_height = (
            torch_int(torch.ceil(orig_height / patch_height) * patch_height)
            if torch.jit.is_tracing()
            else int(math.ceil(orig_height / patch_height) * patch_height)
        )
        new_width = (
            torch_int(torch.ceil(orig_width / patch_width) * patch_width)
            if torch.jit.is_tracing()
            else int(math.ceil(orig_width / patch_width) * patch_width)
        )

        interpolate = False
        if new_width != orig_width or new_height != orig_height:
            # Note: Padding can be done, but then it needs to be handled in attention function.
            features = nn.functional.interpolate(
                features, size=(new_height, new_width), mode="bilinear", align_corners=False
            )
            interpolate = True

        # number of patches along width and height
        num_patch_width = new_width // patch_width
        num_patch_height = new_height // patch_height
        num_patches = num_patch_height * num_patch_width

        # convert from shape (batch_size, channels, orig_height, orig_width)
        # to the shape (batch_size * patch_area, num_patches, channels)
        patches = features.reshape(
            batch_size * channels * num_patch_height, patch_height, num_patch_width, patch_width
        )
        patches = patches.transpose(1, 2)
        patches = patches.reshape(batch_size, channels, num_patches, patch_area)
        patches = patches.transpose(1, 3)
        patches = patches.reshape(batch_size * patch_area, num_patches, -1)

        info_dict = {
            "orig_size": (orig_height, orig_width),
            "batch_size": batch_size,
            "channels": channels,
            "interpolate": interpolate,
            "num_patches": num_patches,
            "num_patches_width": num_patch_width,
            "num_patches_height": num_patch_height,
        }
        return patches, info_dict

    def folding(self, patches: torch.Tensor, info_dict: dict) -> torch.Tensor:
        patch_width, patch_height = self.patch_width, self.patch_height
        patch_area = int(patch_width * patch_height)

        batch_size = info_dict["batch_size"]
        channels = info_dict["channels"]
        num_patches = info_dict["num_patches"]
        num_patch_height = info_dict["num_patches_height"]
        num_patch_width = info_dict["num_patches_width"]

        # convert from shape (batch_size * patch_area, num_patches, channels)
        # back to shape (batch_size, channels, orig_height, orig_width)
        features = patches.contiguous().view(batch_size, patch_area, num_patches, -1)
        features = features.transpose(1, 3)
        features = features.reshape(
            batch_size * channels * num_patch_height, num_patch_width, patch_height, patch_width
        )
        features = features.transpose(1, 2)
        features = features.reshape(
            batch_size, channels, num_patch_height * patch_height, num_patch_width * patch_width
        )

        if info_dict["interpolate"]:
            features = nn.functional.interpolate(
                features, size=info_dict["orig_size"], mode="bilinear", align_corners=False
            )

        return features

    def forward(self, features: torch.Tensor) -> torch.Tensor:
        # reduce spatial dimensions if needed
        if self.downsampling_layer:
            features = self.downsampling_layer(features)

        residual = features

        # local representation
        features = self.conv_kxk(features)
        features = self.conv_1x1(features)

        # convert feature map to patches
        patches, info_dict = self.unfolding(features)

        # learn global representations
        patches = self.transformer(patches)
        patches = self.layernorm(patches)

        # convert patches back to feature maps
        features = self.folding(patches, info_dict)

        features = self.conv_projection(features)
        features = self.fusion(torch.cat((residual, features), dim=1))
        return features


class MobileViTEncoder(nn.Module):
    def __init__(self, config: MobileViTConfig) -> None:
        super().__init__()
        self.config = config

        self.layer = nn.ModuleList()
        self.gradient_checkpointing = False

        # segmentation architectures like DeepLab and PSPNet modify the strides
        # of the classification backbones
        dilate_layer_4 = dilate_layer_5 = False
        if config.output_stride == 8:
            dilate_layer_4 = True
            dilate_layer_5 = True
        elif config.output_stride == 16:
            dilate_layer_5 = True

        dilation = 1

        layer_1 = MobileViTMobileNetLayer(
            config,
            in_channels=config.neck_hidden_sizes[0],
            out_channels=config.neck_hidden_sizes[1],
            stride=1,
            num_stages=1,
        )
        self.layer.append(layer_1)

        layer_2 = MobileViTMobileNetLayer(
            config,
            in_channels=config.neck_hidden_sizes[1],
            out_channels=config.neck_hidden_sizes[2],
            stride=2,
            num_stages=3,
        )
        self.layer.append(layer_2)

        layer_3 = MobileViTLayer(
            config,
            in_channels=config.neck_hidden_sizes[2],
            out_channels=config.neck_hidden_sizes[3],
            stride=2,
            hidden_size=config.hidden_sizes[0],
            num_stages=2,
        )
        self.layer.append(layer_3)

        if dilate_layer_4:
            dilation *= 2

        layer_4 = MobileViTLayer(
            config,
            in_channels=config.neck_hidden_sizes[3],
            out_channels=config.neck_hidden_sizes[4],
            stride=2,
            hidden_size=config.hidden_sizes[1],
            num_stages=4,
            dilation=dilation,
        )
        self.layer.append(layer_4)

        if dilate_layer_5:
            dilation *= 2

        layer_5 = MobileViTLayer(
            config,
            in_channels=config.neck_hidden_sizes[4],
            out_channels=config.neck_hidden_sizes[5],
            stride=2,
            hidden_size=config.hidden_sizes[2],
            num_stages=3,
            dilation=dilation,
        )
        self.layer.append(layer_5)

    def forward(
        self,
        hidden_states: torch.Tensor,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ) -> Union[tuple, BaseModelOutputWithNoAttention]:
        all_hidden_states = () if output_hidden_states else None

        for i, layer_module in enumerate(self.layer):
            hidden_states = layer_module(hidden_states)

            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)

        return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=all_hidden_states)


@auto_docstring
class MobileViTPreTrainedModel(PreTrainedModel):
    config: MobileViTConfig
    base_model_prefix = "mobilevit"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True
    _no_split_modules = ["MobileViTLayer"]

    def _init_weights(self, module: nn.Module) -> None:
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)


@auto_docstring
class MobileViTModel(MobileViTPreTrainedModel):
    def __init__(self, config: MobileViTConfig, expand_output: bool = True):
        r"""
        expand_output (`bool`, *optional*, defaults to `True`):
            Whether to expand the output of the model using a 1x1 convolution. If `True`, the model will apply an additional
            1x1 convolution to expand the output channels from `config.neck_hidden_sizes[5]` to `config.neck_hidden_sizes[6]`.
        """
        super().__init__(config)
        self.config = config
        self.expand_output = expand_output

        self.conv_stem = MobileViTConvLayer(
            config,
            in_channels=config.num_channels,
            out_channels=config.neck_hidden_sizes[0],
            kernel_size=3,
            stride=2,
        )

        self.encoder = MobileViTEncoder(config)

        if self.expand_output:
            self.conv_1x1_exp = MobileViTConvLayer(
                config,
                in_channels=config.neck_hidden_sizes[5],
                out_channels=config.neck_hidden_sizes[6],
                kernel_size=1,
            )

        # Initialize weights and apply final processing
        self.post_init()

    def _prune_heads(self, heads_to_prune):
        """Prunes heads of the model.
        heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel
        """
        for layer_index, heads in heads_to_prune.items():
            mobilevit_layer = self.encoder.layer[layer_index]
            if isinstance(mobilevit_layer, MobileViTLayer):
                for transformer_layer in mobilevit_layer.transformer.layer:
                    transformer_layer.attention.prune_heads(heads)

    @auto_docstring
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if pixel_values is None:
            raise ValueError("You have to specify pixel_values")

        embedding_output = self.conv_stem(pixel_values)

        encoder_outputs = self.encoder(
            embedding_output,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if self.expand_output:
            last_hidden_state = self.conv_1x1_exp(encoder_outputs[0])

            # global average pooling: (batch_size, channels, height, width) -> (batch_size, channels)
            pooled_output = torch.mean(last_hidden_state, dim=[-2, -1], keepdim=False)
        else:
            last_hidden_state = encoder_outputs[0]
            pooled_output = None

        if not return_dict:
            output = (last_hidden_state, pooled_output) if pooled_output is not None else (last_hidden_state,)
            return output + encoder_outputs[1:]

        return BaseModelOutputWithPoolingAndNoAttention(
            last_hidden_state=last_hidden_state,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
        )


@auto_docstring(
    custom_intro="""
    MobileViT model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """
)
class MobileViTForImageClassification(MobileViTPreTrainedModel):
    def __init__(self, config: MobileViTConfig) -> None:
        super().__init__(config)

        self.num_labels = config.num_labels
        self.mobilevit = MobileViTModel(config)

        # Classifier head
        self.dropout = nn.Dropout(config.classifier_dropout_prob, inplace=True)
        self.classifier = (
            nn.Linear(config.neck_hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity()
        )

        # Initialize weights and apply final processing
        self.post_init()

    @auto_docstring
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        labels: Optional[torch.Tensor] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss). If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.mobilevit(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)

        pooled_output = outputs.pooler_output if return_dict else outputs[1]

        logits = self.classifier(self.dropout(pooled_output))

        loss = None
        if labels is not None:
            loss = self.loss_function(labels, logits, self.config)

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return ImageClassifierOutputWithNoAttention(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
        )


class MobileViTASPPPooling(nn.Module):
    def __init__(self, config: MobileViTConfig, in_channels: int, out_channels: int) -> None:
        super().__init__()

        self.global_pool = nn.AdaptiveAvgPool2d(output_size=1)

        self.conv_1x1 = MobileViTConvLayer(
            config,
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=1,
            stride=1,
            use_normalization=True,
            use_activation="relu",
        )

    def forward(self, features: torch.Tensor) -> torch.Tensor:
        spatial_size = features.shape[-2:]
        features = self.global_pool(features)
        features = self.conv_1x1(features)
        features = nn.functional.interpolate(features, size=spatial_size, mode="bilinear", align_corners=False)
        return features


class MobileViTASPP(nn.Module):
    """
    ASPP module defined in DeepLab papers: https://huggingface.co/papers/1606.00915, https://huggingface.co/papers/1706.05587
    """

    def __init__(self, config: MobileViTConfig) -> None:
        super().__init__()

        in_channels = config.neck_hidden_sizes[-2]
        out_channels = config.aspp_out_channels

        if len(config.atrous_rates) != 3:
            raise ValueError("Expected 3 values for atrous_rates")

        self.convs = nn.ModuleList()

        in_projection = MobileViTConvLayer(
            config,
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=1,
            use_activation="relu",
        )
        self.convs.append(in_projection)

        self.convs.extend(
            [
                MobileViTConvLayer(
                    config,
                    in_channels=in_channels,
                    out_channels=out_channels,
                    kernel_size=3,
                    dilation=rate,
                    use_activation="relu",
                )
                for rate in config.atrous_rates
            ]
        )

        pool_layer = MobileViTASPPPooling(config, in_channels, out_channels)
        self.convs.append(pool_layer)

        self.project = MobileViTConvLayer(
            config, in_channels=5 * out_channels, out_channels=out_channels, kernel_size=1, use_activation="relu"
        )

        self.dropout = nn.Dropout(p=config.aspp_dropout_prob)

    def forward(self, features: torch.Tensor) -> torch.Tensor:
        pyramid = []
        for conv in self.convs:
            pyramid.append(conv(features))
        pyramid = torch.cat(pyramid, dim=1)

        pooled_features = self.project(pyramid)
        pooled_features = self.dropout(pooled_features)
        return pooled_features


class MobileViTDeepLabV3(nn.Module):
    """
    DeepLabv3 architecture: https://huggingface.co/papers/1706.05587
    """

    def __init__(self, config: MobileViTConfig) -> None:
        super().__init__()
        self.aspp = MobileViTASPP(config)

        self.dropout = nn.Dropout2d(config.classifier_dropout_prob)

        self.classifier = MobileViTConvLayer(
            config,
            in_channels=config.aspp_out_channels,
            out_channels=config.num_labels,
            kernel_size=1,
            use_normalization=False,
            use_activation=False,
            bias=True,
        )

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        features = self.aspp(hidden_states[-1])
        features = self.dropout(features)
        features = self.classifier(features)
        return features


@auto_docstring(
    custom_intro="""
    MobileViT model with a semantic segmentation head on top, e.g. for Pascal VOC.
    """
)
class MobileViTForSemanticSegmentation(MobileViTPreTrainedModel):
    def __init__(self, config: MobileViTConfig) -> None:
        super().__init__(config)

        self.num_labels = config.num_labels
        self.mobilevit = MobileViTModel(config, expand_output=False)
        self.segmentation_head = MobileViTDeepLabV3(config)

        # Initialize weights and apply final processing
        self.post_init()

    @auto_docstring
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, SemanticSegmenterOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
            Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).

        Examples:

        ```python
        >>> import requests
        >>> import torch
        >>> from PIL import Image
        >>> from transformers import AutoImageProcessor, MobileViTForSemanticSegmentation

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> image_processor = AutoImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-small")
        >>> model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-small")

        >>> inputs = image_processor(images=image, return_tensors="pt")

        >>> with torch.no_grad():
        ...     outputs = model(**inputs)

        >>> # logits are of shape (batch_size, num_labels, height, width)
        >>> logits = outputs.logits
        ```"""
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if labels is not None and self.config.num_labels == 1:
            raise ValueError("The number of labels should be greater than one")

        outputs = self.mobilevit(
            pixel_values,
            output_hidden_states=True,  # we need the intermediate hidden states
            return_dict=return_dict,
        )

        encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1]

        logits = self.segmentation_head(encoder_hidden_states)

        loss = None
        if labels is not None:
            # upsample logits to the images' original size
            upsampled_logits = nn.functional.interpolate(
                logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
            )
            loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index)
            loss = loss_fct(upsampled_logits, labels)

        if not return_dict:
            if output_hidden_states:
                output = (logits,) + outputs[1:]
            else:
                output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return SemanticSegmenterOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states if output_hidden_states else None,
            attentions=None,
        )


__all__ = [
    "MobileViTForImageClassification",
    "MobileViTForSemanticSegmentation",
    "MobileViTModel",
    "MobileViTPreTrainedModel",
]
